[**@lancedb/lancedb**](../README.md) • **Docs**

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[@lancedb/lancedb](../globals.md) / makeArrowTable

# Function: makeArrowTable()

```ts
function makeArrowTable(
   data,
   options?,
   metadata?): ArrowTable
```

An enhanced version of the apache-arrow makeTable function from Apache Arrow
that supports nested fields and embeddings columns.

(typically you do not need to call this function.  It will be called automatically
when creating a table or adding data to it)

This function converts an array of Record<String, any> (row-major JS objects)
to an Arrow Table (a columnar structure)

If a schema is provided then it will be used to determine the resulting array
types.  Fields will also be reordered to fit the order defined by the schema.

If a schema is not provided then the types will be inferred and the field order
will be controlled by the order of properties in the first record.  If a type
is inferred it will always be nullable.

If not all fields are found in the data, then a subset of the schema will be
returned.

If the input is empty then a schema must be provided to create an empty table.

When a schema is not specified then data types will be inferred.  The inference
rules are as follows:

 - boolean => Bool
 - number => Float64
 - bigint => Int64
 - String => Utf8
 - Buffer => Binary
 - Record<String, any> => Struct
 - Array<any> => List

## Parameters

* **data**: `Record`&lt;`string`, `unknown`&gt;[]

* **options?**: `Partial`&lt;[`MakeArrowTableOptions`](../classes/MakeArrowTableOptions.md)&gt;

* **metadata?**: `Map`&lt;`string`, `string`&gt;

## Returns

`ArrowTable`

## Example

```ts
import { fromTableToBuffer, makeArrowTable } from "../arrow";
import { Field, FixedSizeList, Float16, Float32, Int32, Schema } from "apache-arrow";

const schema = new Schema([
  new Field("a", new Int32()),
  new Field("b", new Float32()),
  new Field("c", new FixedSizeList(3, new Field("item", new Float16()))),
 ]);
 const table = makeArrowTable([
   { a: 1, b: 2, c: [1, 2, 3] },
   { a: 4, b: 5, c: [4, 5, 6] },
   { a: 7, b: 8, c: [7, 8, 9] },
 ], { schema });
```

By default it assumes that the column named `vector` is a vector column
and it will be converted into a fixed size list array of type float32.
The `vectorColumns` option can be used to support other vector column
names and data types.

```ts
const schema = new Schema([
  new Field("a", new Float64()),
  new Field("b", new Float64()),
  new Field(
    "vector",
    new FixedSizeList(3, new Field("item", new Float32()))
  ),
]);
const table = makeArrowTable([
  { a: 1, b: 2, vector: [1, 2, 3] },
  { a: 4, b: 5, vector: [4, 5, 6] },
  { a: 7, b: 8, vector: [7, 8, 9] },
]);
assert.deepEqual(table.schema, schema);
```

You can specify the vector column types and names using the options as well

```ts
const schema = new Schema([
  new Field('a', new Float64()),
  new Field('b', new Float64()),
  new Field('vec1', new FixedSizeList(3, new Field('item', new Float16()))),
  new Field('vec2', new FixedSizeList(3, new Field('item', new Float16())))
]);
const table = makeArrowTable([
  { a: 1, b: 2, vec1: [1, 2, 3], vec2: [2, 4, 6] },
  { a: 4, b: 5, vec1: [4, 5, 6], vec2: [8, 10, 12] },
  { a: 7, b: 8, vec1: [7, 8, 9], vec2: [14, 16, 18] }
], {
  vectorColumns: {
    vec1: { type: new Float16() },
    vec2: { type: new Float16() }
  }
}
assert.deepEqual(table.schema, schema)
```